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raulgdp
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roberta-large-ner-qlorafinetune-runs-colab

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xlm-roberta
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README.md CHANGED
@@ -23,11 +23,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the biobert_json dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0714
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- - Precision: 0.9376
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- - Recall: 0.9577
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- - F1: 0.9475
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- - Accuracy: 0.9816
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  ## Model description
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@@ -50,111 +50,112 @@ The following hyperparameters were used during training:
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
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- - optimizer: Use adamw_hf with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - training_steps: 1820
 
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  ### Training results
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59
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
60
  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
61
- | 1.8474 | 0.0654 | 20 | 1.0603 | 0.1875 | 0.0011 | 0.0021 | 0.7190 |
62
- | 0.8452 | 0.1307 | 40 | 0.4043 | 0.7626 | 0.7118 | 0.7363 | 0.9008 |
63
- | 0.3976 | 0.1961 | 60 | 0.2316 | 0.8472 | 0.8189 | 0.8328 | 0.9394 |
64
- | 0.2858 | 0.2614 | 80 | 0.1675 | 0.8339 | 0.8730 | 0.8530 | 0.9517 |
65
- | 0.2182 | 0.3268 | 100 | 0.1548 | 0.8215 | 0.9088 | 0.8629 | 0.9545 |
66
- | 0.22 | 0.3922 | 120 | 0.1487 | 0.848 | 0.9135 | 0.8795 | 0.9579 |
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- | 0.1615 | 0.4575 | 140 | 0.1143 | 0.8977 | 0.9068 | 0.9022 | 0.9654 |
68
- | 0.1566 | 0.5229 | 160 | 0.1000 | 0.8983 | 0.9229 | 0.9104 | 0.9690 |
69
- | 0.1561 | 0.5882 | 180 | 0.1178 | 0.8882 | 0.9459 | 0.9161 | 0.9677 |
70
- | 0.123 | 0.6536 | 200 | 0.0923 | 0.9087 | 0.9422 | 0.9251 | 0.9731 |
71
- | 0.131 | 0.7190 | 220 | 0.0949 | 0.9224 | 0.9129 | 0.9176 | 0.9706 |
72
- | 0.1272 | 0.7843 | 240 | 0.1026 | 0.8983 | 0.9555 | 0.9260 | 0.9709 |
73
- | 0.1201 | 0.8497 | 260 | 0.0893 | 0.9219 | 0.9386 | 0.9302 | 0.9738 |
74
- | 0.1167 | 0.9150 | 280 | 0.1038 | 0.8895 | 0.9381 | 0.9131 | 0.9700 |
75
- | 0.1293 | 0.9804 | 300 | 0.0869 | 0.9160 | 0.9455 | 0.9306 | 0.9747 |
76
- | 0.1037 | 1.0458 | 320 | 0.0837 | 0.9080 | 0.9348 | 0.9212 | 0.9735 |
77
- | 0.0835 | 1.1111 | 340 | 0.0871 | 0.9163 | 0.9609 | 0.9381 | 0.9758 |
78
- | 0.0846 | 1.1765 | 360 | 0.0853 | 0.9166 | 0.9496 | 0.9328 | 0.9758 |
79
- | 0.0942 | 1.2418 | 380 | 0.0767 | 0.9310 | 0.9497 | 0.9403 | 0.9792 |
80
- | 0.0988 | 1.3072 | 400 | 0.0807 | 0.9197 | 0.9554 | 0.9372 | 0.9770 |
81
- | 0.0977 | 1.3725 | 420 | 0.0838 | 0.9131 | 0.9305 | 0.9217 | 0.9730 |
82
- | 0.0882 | 1.4379 | 440 | 0.0787 | 0.9323 | 0.9542 | 0.9431 | 0.9788 |
83
- | 0.0838 | 1.5033 | 460 | 0.0697 | 0.9319 | 0.9521 | 0.9419 | 0.9791 |
84
- | 0.0974 | 1.5686 | 480 | 0.0775 | 0.9238 | 0.9625 | 0.9428 | 0.9782 |
85
- | 0.077 | 1.6340 | 500 | 0.0796 | 0.9254 | 0.9562 | 0.9405 | 0.9778 |
86
- | 0.069 | 1.6993 | 520 | 0.0864 | 0.9106 | 0.9648 | 0.9369 | 0.9756 |
87
- | 0.0757 | 1.7647 | 540 | 0.0811 | 0.9071 | 0.9378 | 0.9222 | 0.9745 |
88
- | 0.0863 | 1.8301 | 560 | 0.0756 | 0.9195 | 0.9613 | 0.9400 | 0.9785 |
89
- | 0.0903 | 1.8954 | 580 | 0.0992 | 0.8900 | 0.9542 | 0.9210 | 0.9703 |
90
- | 0.0845 | 1.9608 | 600 | 0.0799 | 0.9209 | 0.9562 | 0.9382 | 0.9776 |
91
- | 0.0702 | 2.0261 | 620 | 0.0749 | 0.9294 | 0.9563 | 0.9427 | 0.9785 |
92
- | 0.0723 | 2.0915 | 640 | 0.0711 | 0.9284 | 0.9603 | 0.9440 | 0.9800 |
93
- | 0.0509 | 2.1569 | 660 | 0.0750 | 0.9232 | 0.9473 | 0.9351 | 0.9778 |
94
- | 0.079 | 2.2222 | 680 | 0.0727 | 0.9358 | 0.9542 | 0.9449 | 0.9797 |
95
- | 0.0702 | 2.2876 | 700 | 0.0750 | 0.9306 | 0.9609 | 0.9455 | 0.9800 |
96
- | 0.0533 | 2.3529 | 720 | 0.0694 | 0.9365 | 0.9585 | 0.9474 | 0.9808 |
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- | 0.0626 | 2.4183 | 740 | 0.0685 | 0.9346 | 0.9556 | 0.9450 | 0.9803 |
98
- | 0.0797 | 2.4837 | 760 | 0.0711 | 0.9293 | 0.9586 | 0.9437 | 0.9795 |
99
- | 0.0619 | 2.5490 | 780 | 0.0749 | 0.9293 | 0.9623 | 0.9455 | 0.9803 |
100
- | 0.0685 | 2.6144 | 800 | 0.0645 | 0.9406 | 0.9552 | 0.9479 | 0.9819 |
101
- | 0.0606 | 2.6797 | 820 | 0.0711 | 0.9381 | 0.9506 | 0.9443 | 0.9802 |
102
- | 0.0624 | 2.7451 | 840 | 0.0703 | 0.9329 | 0.9537 | 0.9432 | 0.9809 |
103
- | 0.0572 | 2.8105 | 860 | 0.0704 | 0.9356 | 0.9617 | 0.9485 | 0.9816 |
104
- | 0.0656 | 2.8758 | 880 | 0.0747 | 0.9312 | 0.9557 | 0.9433 | 0.9793 |
105
- | 0.0578 | 2.9412 | 900 | 0.0729 | 0.9272 | 0.9631 | 0.9448 | 0.9794 |
106
- | 0.0527 | 3.0065 | 920 | 0.0710 | 0.9332 | 0.9655 | 0.9491 | 0.9814 |
107
- | 0.0451 | 3.0719 | 940 | 0.0706 | 0.9335 | 0.9610 | 0.9470 | 0.9807 |
108
- | 0.0452 | 3.1373 | 960 | 0.0777 | 0.9286 | 0.9594 | 0.9438 | 0.9792 |
109
- | 0.0448 | 3.2026 | 980 | 0.0727 | 0.9326 | 0.9493 | 0.9409 | 0.9797 |
110
- | 0.0529 | 3.2680 | 1000 | 0.0769 | 0.9347 | 0.9552 | 0.9448 | 0.9791 |
111
- | 0.0479 | 3.3333 | 1020 | 0.0764 | 0.9273 | 0.9533 | 0.9402 | 0.9788 |
112
- | 0.0488 | 3.3987 | 1040 | 0.0784 | 0.9271 | 0.9661 | 0.9462 | 0.9802 |
113
- | 0.0539 | 3.4641 | 1060 | 0.0772 | 0.9231 | 0.9570 | 0.9398 | 0.9787 |
114
- | 0.0465 | 3.5294 | 1080 | 0.0730 | 0.9269 | 0.9551 | 0.9408 | 0.9796 |
115
- | 0.0453 | 3.5948 | 1100 | 0.0723 | 0.9347 | 0.9575 | 0.9460 | 0.9806 |
116
- | 0.0397 | 3.6601 | 1120 | 0.0722 | 0.9342 | 0.9562 | 0.9451 | 0.9812 |
117
- | 0.0567 | 3.7255 | 1140 | 0.0685 | 0.9420 | 0.9618 | 0.9518 | 0.9824 |
118
- | 0.0501 | 3.7908 | 1160 | 0.0662 | 0.9444 | 0.9629 | 0.9535 | 0.9833 |
119
- | 0.0561 | 3.8562 | 1180 | 0.0774 | 0.9323 | 0.9605 | 0.9462 | 0.9796 |
120
- | 0.0512 | 3.9216 | 1200 | 0.0752 | 0.935 | 0.9624 | 0.9485 | 0.9801 |
121
- | 0.0545 | 3.9869 | 1220 | 0.0662 | 0.9374 | 0.9533 | 0.9453 | 0.9815 |
122
- | 0.0423 | 4.0523 | 1240 | 0.0735 | 0.9332 | 0.9593 | 0.9461 | 0.9805 |
123
- | 0.0344 | 4.1176 | 1260 | 0.0726 | 0.9290 | 0.9575 | 0.9431 | 0.9802 |
124
- | 0.0386 | 4.1830 | 1280 | 0.0690 | 0.9373 | 0.9647 | 0.9508 | 0.9825 |
125
- | 0.0411 | 4.2484 | 1300 | 0.0692 | 0.9400 | 0.9577 | 0.9488 | 0.9819 |
126
- | 0.0394 | 4.3137 | 1320 | 0.0680 | 0.9444 | 0.9555 | 0.9499 | 0.9822 |
127
- | 0.0427 | 4.3791 | 1340 | 0.0701 | 0.9371 | 0.9577 | 0.9473 | 0.9811 |
128
- | 0.035 | 4.4444 | 1360 | 0.0702 | 0.9353 | 0.9558 | 0.9455 | 0.9805 |
129
- | 0.0312 | 4.5098 | 1380 | 0.0739 | 0.9295 | 0.9576 | 0.9433 | 0.9803 |
130
- | 0.0419 | 4.5752 | 1400 | 0.0698 | 0.9375 | 0.9538 | 0.9456 | 0.9812 |
131
- | 0.0369 | 4.6405 | 1420 | 0.0708 | 0.9350 | 0.9611 | 0.9479 | 0.9814 |
132
- | 0.0316 | 4.7059 | 1440 | 0.0718 | 0.9388 | 0.9641 | 0.9513 | 0.9821 |
133
- | 0.0438 | 4.7712 | 1460 | 0.0749 | 0.9343 | 0.9611 | 0.9475 | 0.9805 |
134
- | 0.0399 | 4.8366 | 1480 | 0.0710 | 0.9414 | 0.9611 | 0.9511 | 0.9815 |
135
- | 0.0381 | 4.9020 | 1500 | 0.0722 | 0.9370 | 0.9625 | 0.9496 | 0.9814 |
136
- | 0.0475 | 4.9673 | 1520 | 0.0708 | 0.9380 | 0.9562 | 0.9470 | 0.9810 |
137
- | 0.0319 | 5.0327 | 1540 | 0.0755 | 0.9296 | 0.9589 | 0.9440 | 0.9796 |
138
- | 0.0315 | 5.0980 | 1560 | 0.0710 | 0.9386 | 0.9587 | 0.9485 | 0.9815 |
139
- | 0.0352 | 5.1634 | 1580 | 0.0728 | 0.9356 | 0.9600 | 0.9477 | 0.9810 |
140
- | 0.029 | 5.2288 | 1600 | 0.0718 | 0.9389 | 0.9556 | 0.9471 | 0.9811 |
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- | 0.0287 | 5.2941 | 1620 | 0.0730 | 0.9365 | 0.9589 | 0.9476 | 0.9810 |
142
- | 0.0271 | 5.3595 | 1640 | 0.0721 | 0.9394 | 0.9592 | 0.9492 | 0.9815 |
143
- | 0.0315 | 5.4248 | 1660 | 0.0708 | 0.9401 | 0.9599 | 0.9499 | 0.9820 |
144
- | 0.0364 | 5.4902 | 1680 | 0.0715 | 0.9372 | 0.9572 | 0.9471 | 0.9814 |
145
- | 0.0306 | 5.5556 | 1700 | 0.0700 | 0.9424 | 0.9594 | 0.9508 | 0.9821 |
146
- | 0.0325 | 5.6209 | 1720 | 0.0717 | 0.9387 | 0.9606 | 0.9495 | 0.9817 |
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- | 0.0303 | 5.6863 | 1740 | 0.0711 | 0.9397 | 0.9591 | 0.9493 | 0.9818 |
148
- | 0.0301 | 5.7516 | 1760 | 0.0707 | 0.9396 | 0.9581 | 0.9487 | 0.9817 |
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- | 0.0251 | 5.8170 | 1780 | 0.0715 | 0.9369 | 0.9575 | 0.9471 | 0.9815 |
150
- | 0.0337 | 5.8824 | 1800 | 0.0717 | 0.9364 | 0.9572 | 0.9467 | 0.9814 |
151
- | 0.0269 | 5.9477 | 1820 | 0.0714 | 0.9376 | 0.9577 | 0.9475 | 0.9816 |
152
 
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154
  ### Framework versions
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  - PEFT 0.13.2
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- - Transformers 4.47.0
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  - Pytorch 2.5.1+cu121
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  - Datasets 3.2.0
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  - Tokenizers 0.21.0
 
23
 
24
  This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the biobert_json dataset.
25
  It achieves the following results on the evaluation set:
26
+ - Loss: 0.0739
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+ - Precision: 0.9386
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+ - Recall: 0.9569
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+ - F1: 0.9477
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+ - Accuracy: 0.9814
31
 
32
  ## Model description
33
 
 
50
  - train_batch_size: 32
51
  - eval_batch_size: 32
52
  - seed: 42
53
+ - optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - training_steps: 1820
56
+ - mixed_precision_training: Native AMP
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58
  ### Training results
59
 
60
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
61
  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
62
+ | 1.9504 | 0.0654 | 20 | 1.0288 | 0.3604 | 0.0910 | 0.1453 | 0.7415 |
63
+ | 0.7946 | 0.1307 | 40 | 0.3959 | 0.7968 | 0.7284 | 0.7611 | 0.9014 |
64
+ | 0.4228 | 0.1961 | 60 | 0.2336 | 0.8281 | 0.8316 | 0.8299 | 0.9388 |
65
+ | 0.2852 | 0.2614 | 80 | 0.1685 | 0.8513 | 0.8659 | 0.8585 | 0.9529 |
66
+ | 0.2134 | 0.3268 | 100 | 0.1356 | 0.8688 | 0.9124 | 0.8901 | 0.9603 |
67
+ | 0.2123 | 0.3922 | 120 | 0.1180 | 0.8710 | 0.9179 | 0.8938 | 0.9638 |
68
+ | 0.1543 | 0.4575 | 140 | 0.0979 | 0.9015 | 0.9275 | 0.9143 | 0.9698 |
69
+ | 0.1421 | 0.5229 | 160 | 0.1187 | 0.8803 | 0.9405 | 0.9094 | 0.9677 |
70
+ | 0.1587 | 0.5882 | 180 | 0.1135 | 0.8833 | 0.9317 | 0.9069 | 0.9667 |
71
+ | 0.1308 | 0.6536 | 200 | 0.0990 | 0.8985 | 0.9364 | 0.9171 | 0.9711 |
72
+ | 0.134 | 0.7190 | 220 | 0.0930 | 0.9204 | 0.9123 | 0.9163 | 0.9714 |
73
+ | 0.1269 | 0.7843 | 240 | 0.0979 | 0.9097 | 0.9484 | 0.9286 | 0.9724 |
74
+ | 0.1117 | 0.8497 | 260 | 0.0879 | 0.9221 | 0.9366 | 0.9293 | 0.9740 |
75
+ | 0.1179 | 0.9150 | 280 | 0.0952 | 0.9086 | 0.9488 | 0.9283 | 0.9731 |
76
+ | 0.1256 | 0.9804 | 300 | 0.0824 | 0.9253 | 0.9414 | 0.9333 | 0.9755 |
77
+ | 0.1082 | 1.0458 | 320 | 0.0826 | 0.9198 | 0.9454 | 0.9324 | 0.9755 |
78
+ | 0.1043 | 1.1111 | 340 | 0.0940 | 0.9117 | 0.9615 | 0.9359 | 0.9732 |
79
+ | 0.0932 | 1.1765 | 360 | 0.0823 | 0.9202 | 0.9570 | 0.9383 | 0.9768 |
80
+ | 0.0984 | 1.2418 | 380 | 0.0728 | 0.9278 | 0.9560 | 0.9416 | 0.9790 |
81
+ | 0.1068 | 1.3072 | 400 | 0.0779 | 0.9294 | 0.9483 | 0.9387 | 0.9774 |
82
+ | 0.1052 | 1.3725 | 420 | 0.0832 | 0.9008 | 0.9324 | 0.9163 | 0.9736 |
83
+ | 0.0903 | 1.4379 | 440 | 0.0794 | 0.9333 | 0.9460 | 0.9396 | 0.9774 |
84
+ | 0.0908 | 1.5033 | 460 | 0.0708 | 0.9319 | 0.9531 | 0.9424 | 0.9785 |
85
+ | 0.0963 | 1.5686 | 480 | 0.0914 | 0.9220 | 0.9570 | 0.9392 | 0.9766 |
86
+ | 0.086 | 1.6340 | 500 | 0.0839 | 0.9198 | 0.9525 | 0.9358 | 0.9765 |
87
+ | 0.0791 | 1.6993 | 520 | 0.0838 | 0.9151 | 0.9627 | 0.9383 | 0.9767 |
88
+ | 0.0798 | 1.7647 | 540 | 0.0754 | 0.9292 | 0.9526 | 0.9408 | 0.9795 |
89
+ | 0.0874 | 1.8301 | 560 | 0.0718 | 0.9343 | 0.9585 | 0.9462 | 0.9803 |
90
+ | 0.0851 | 1.8954 | 580 | 0.0839 | 0.9155 | 0.9515 | 0.9332 | 0.9753 |
91
+ | 0.0809 | 1.9608 | 600 | 0.0770 | 0.9308 | 0.9534 | 0.9420 | 0.9793 |
92
+ | 0.0714 | 2.0261 | 620 | 0.0802 | 0.9278 | 0.9616 | 0.9444 | 0.9789 |
93
+ | 0.062 | 2.0915 | 640 | 0.0697 | 0.9360 | 0.9579 | 0.9468 | 0.9805 |
94
+ | 0.0523 | 2.1569 | 660 | 0.0737 | 0.9308 | 0.9536 | 0.9421 | 0.9792 |
95
+ | 0.073 | 2.2222 | 680 | 0.0730 | 0.9287 | 0.9591 | 0.9436 | 0.9793 |
96
+ | 0.0659 | 2.2876 | 700 | 0.0778 | 0.9253 | 0.9583 | 0.9416 | 0.9790 |
97
+ | 0.0551 | 2.3529 | 720 | 0.0731 | 0.9376 | 0.9564 | 0.9469 | 0.9800 |
98
+ | 0.0622 | 2.4183 | 740 | 0.0760 | 0.9325 | 0.9506 | 0.9414 | 0.9788 |
99
+ | 0.0877 | 2.4837 | 760 | 0.0722 | 0.9331 | 0.9582 | 0.9455 | 0.9795 |
100
+ | 0.0668 | 2.5490 | 780 | 0.0727 | 0.9384 | 0.9601 | 0.9491 | 0.9812 |
101
+ | 0.0699 | 2.6144 | 800 | 0.0706 | 0.9395 | 0.9575 | 0.9484 | 0.9814 |
102
+ | 0.0603 | 2.6797 | 820 | 0.0755 | 0.9341 | 0.9536 | 0.9437 | 0.9788 |
103
+ | 0.0652 | 2.7451 | 840 | 0.0761 | 0.9268 | 0.9522 | 0.9394 | 0.9786 |
104
+ | 0.0613 | 2.8105 | 860 | 0.0740 | 0.9337 | 0.9615 | 0.9474 | 0.9807 |
105
+ | 0.065 | 2.8758 | 880 | 0.0758 | 0.9320 | 0.9527 | 0.9422 | 0.9789 |
106
+ | 0.0573 | 2.9412 | 900 | 0.0727 | 0.9297 | 0.9592 | 0.9442 | 0.9798 |
107
+ | 0.0581 | 3.0065 | 920 | 0.0689 | 0.9411 | 0.9595 | 0.9502 | 0.9822 |
108
+ | 0.0438 | 3.0719 | 940 | 0.0711 | 0.9373 | 0.9582 | 0.9476 | 0.9822 |
109
+ | 0.0472 | 3.1373 | 960 | 0.0823 | 0.9317 | 0.9554 | 0.9434 | 0.9792 |
110
+ | 0.0465 | 3.2026 | 980 | 0.0821 | 0.9381 | 0.9500 | 0.9440 | 0.9795 |
111
+ | 0.0458 | 3.2680 | 1000 | 0.0781 | 0.9241 | 0.9481 | 0.9359 | 0.9779 |
112
+ | 0.053 | 3.3333 | 1020 | 0.0724 | 0.9314 | 0.9463 | 0.9388 | 0.9791 |
113
+ | 0.0508 | 3.3987 | 1040 | 0.0794 | 0.9242 | 0.9651 | 0.9442 | 0.9798 |
114
+ | 0.0563 | 3.4641 | 1060 | 0.0758 | 0.9214 | 0.9488 | 0.9349 | 0.9775 |
115
+ | 0.0441 | 3.5294 | 1080 | 0.0745 | 0.9296 | 0.9499 | 0.9396 | 0.9799 |
116
+ | 0.0469 | 3.5948 | 1100 | 0.0751 | 0.9305 | 0.9546 | 0.9424 | 0.9798 |
117
+ | 0.0414 | 3.6601 | 1120 | 0.0700 | 0.9379 | 0.9558 | 0.9468 | 0.9817 |
118
+ | 0.0599 | 3.7255 | 1140 | 0.0704 | 0.9421 | 0.9561 | 0.9490 | 0.9813 |
119
+ | 0.0492 | 3.7908 | 1160 | 0.0732 | 0.9385 | 0.9551 | 0.9467 | 0.9812 |
120
+ | 0.0588 | 3.8562 | 1180 | 0.0789 | 0.9268 | 0.9604 | 0.9433 | 0.9786 |
121
+ | 0.0563 | 3.9216 | 1200 | 0.0754 | 0.9351 | 0.9560 | 0.9454 | 0.9797 |
122
+ | 0.0589 | 3.9869 | 1220 | 0.0676 | 0.9415 | 0.9554 | 0.9484 | 0.9817 |
123
+ | 0.0567 | 4.0523 | 1240 | 0.0723 | 0.9343 | 0.9568 | 0.9454 | 0.9804 |
124
+ | 0.0366 | 4.1176 | 1260 | 0.0725 | 0.9337 | 0.9562 | 0.9448 | 0.9804 |
125
+ | 0.0407 | 4.1830 | 1280 | 0.0716 | 0.9402 | 0.9585 | 0.9493 | 0.9818 |
126
+ | 0.0423 | 4.2484 | 1300 | 0.0721 | 0.9400 | 0.9546 | 0.9473 | 0.9808 |
127
+ | 0.0412 | 4.3137 | 1320 | 0.0712 | 0.9429 | 0.9506 | 0.9467 | 0.9814 |
128
+ | 0.0451 | 4.3791 | 1340 | 0.0728 | 0.9350 | 0.9554 | 0.9451 | 0.9806 |
129
+ | 0.0403 | 4.4444 | 1360 | 0.0713 | 0.9347 | 0.9536 | 0.9440 | 0.9804 |
130
+ | 0.0332 | 4.5098 | 1380 | 0.0756 | 0.9328 | 0.9585 | 0.9455 | 0.9806 |
131
+ | 0.0434 | 4.5752 | 1400 | 0.0739 | 0.9375 | 0.9538 | 0.9456 | 0.9807 |
132
+ | 0.0397 | 4.6405 | 1420 | 0.0738 | 0.9359 | 0.9582 | 0.9470 | 0.9810 |
133
+ | 0.0331 | 4.7059 | 1440 | 0.0780 | 0.9310 | 0.9554 | 0.9430 | 0.9796 |
134
+ | 0.0449 | 4.7712 | 1460 | 0.0759 | 0.9367 | 0.9564 | 0.9465 | 0.9805 |
135
+ | 0.0389 | 4.8366 | 1480 | 0.0731 | 0.9381 | 0.9589 | 0.9484 | 0.9812 |
136
+ | 0.0376 | 4.9020 | 1500 | 0.0721 | 0.9361 | 0.9588 | 0.9473 | 0.9809 |
137
+ | 0.0483 | 4.9673 | 1520 | 0.0733 | 0.9378 | 0.9549 | 0.9463 | 0.9811 |
138
+ | 0.0313 | 5.0327 | 1540 | 0.0771 | 0.9353 | 0.9585 | 0.9467 | 0.9807 |
139
+ | 0.0316 | 5.0980 | 1560 | 0.0744 | 0.9405 | 0.9577 | 0.9491 | 0.9813 |
140
+ | 0.037 | 5.1634 | 1580 | 0.0735 | 0.9395 | 0.9588 | 0.9491 | 0.9818 |
141
+ | 0.0313 | 5.2288 | 1600 | 0.0737 | 0.9385 | 0.9585 | 0.9484 | 0.9815 |
142
+ | 0.0303 | 5.2941 | 1620 | 0.0747 | 0.9386 | 0.9581 | 0.9482 | 0.9813 |
143
+ | 0.029 | 5.3595 | 1640 | 0.0746 | 0.9405 | 0.9593 | 0.9498 | 0.9818 |
144
+ | 0.0323 | 5.4248 | 1660 | 0.0723 | 0.9421 | 0.9582 | 0.9501 | 0.9821 |
145
+ | 0.0352 | 5.4902 | 1680 | 0.0732 | 0.9410 | 0.9572 | 0.9490 | 0.9818 |
146
+ | 0.0296 | 5.5556 | 1700 | 0.0721 | 0.9440 | 0.9582 | 0.9511 | 0.9823 |
147
+ | 0.0311 | 5.6209 | 1720 | 0.0747 | 0.9361 | 0.9550 | 0.9454 | 0.9808 |
148
+ | 0.0309 | 5.6863 | 1740 | 0.0735 | 0.9389 | 0.9562 | 0.9475 | 0.9812 |
149
+ | 0.0302 | 5.7516 | 1760 | 0.0733 | 0.9405 | 0.9570 | 0.9487 | 0.9816 |
150
+ | 0.0288 | 5.8170 | 1780 | 0.0742 | 0.9381 | 0.9572 | 0.9475 | 0.9814 |
151
+ | 0.0338 | 5.8824 | 1800 | 0.0743 | 0.9383 | 0.9569 | 0.9475 | 0.9813 |
152
+ | 0.0265 | 5.9477 | 1820 | 0.0739 | 0.9386 | 0.9569 | 0.9477 | 0.9814 |
153
 
154
 
155
  ### Framework versions
156
 
157
  - PEFT 0.13.2
158
+ - Transformers 4.47.1
159
  - Pytorch 2.5.1+cu121
160
  - Datasets 3.2.0
161
  - Tokenizers 0.21.0
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